Another remarkable point is that, we find that even with larger number of learnable parameter than [1]}, our method still outperforms. As shown in Table REF , the models with larger number of parameters tend to perform worse. It is well known that the number of parameters has inverse relation to generalization power [2]}. However, VAT successfully avoids this by focusing on cost aggregation, showing that simply reducing the number of parameters may not be the answer.
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